Data are collected in 5 separate workspace, one for the main density data calculations across the
space and 4 for the subproject simulations that were performed to validate and dive deeper into
specific engine implementations. In order to copy the simulation trajectory and calculated averages
used to generate figures, these workspace folders must be downloaded and pointed to the correct
place in the GitHub Project Structure, which can be found at
https://github.com/mosdef-hub/reproducibility_study and Each compressed file contains the data for a single workspace.
Nanoparticles (NPs) formed in nonthermal plasmas (NTPs) can have unique properties and applications. However, modeling their growth in these environments presents significant challenges due to the non-equilibrium nature of NTPs, making them computationally expensive to describe. In this work, we address the challenges associated with accelerating the estimation of parameters needed for these models. Specifically, we explore how different machine learning models can be tailored to improve prediction outcomes. We apply these methods to reactive classical molecular dynamics data, which capture the processes associated with colliding silane fragments in NTPs. These reactions exemplify processes where qualitative trends are clear, but their quantification is challenging, hard to generalize, and requires time-consuming simulations. Our results demonstrate that good prediction performance can be achieved when appropriate loss functions are implemented and correct invariances are imposed. While the diversity of molecules used in the training set is critical for accurate prediction, our findings indicate that only a fraction (15-25%) of the energy and temperature sampling is required to achieve high levels of accuracy. This suggests a substantial reduction in computational effort is possible for similar systems.
